Information alone is not enough to bring about change.
Decision intelligence is the optimization of the processes by which decisions are made, managed, monitored, and continuously iterated.
Other considerations include:
- What is the most impactful decision for your business?
- Where are the operational bottlenecks?
- What processes require human decision-making?
- Where does AI decision support play a role?
- What actions are safe to automate?
- What are the new governance requirements?
Such considerations become especially important as the involvement of AI agents increases.
If proper workflow redesign is not accompanied by governance, there is a risk that tasks will be automated without improving overall performance.
This is an issue that is becoming increasingly vocal among industry analysts. In this regard, Gartner notes that many in-house AI agent projects may not be able to achieve desired results without governance and management in place. This is because as AI agents increasingly take on responsibility for coordinating tasks within the system, some guardrails will need to be put in place as far as decision-making is concerned.
I have worked with companies that have been able to reduce service resolution times and improve operational agility only by focusing AI-powered processes directly on key business metrics such as reducing cycle times, avoiding escalations, increasing profit margins, and customer retention.
Many companies are now focused on moving from experimentation to measurable operational impact.
Fragmented AI produces fragmented results
One of the key operational challenges I consistently encounter is fragmented intelligence within the enterprise.
Sales uses one set of AI solutions. Customer service uses a different set of AI solutions. Yet another set of predictive models is used in the supply chain. Financial analysis works within a completely different set of AI workflows.
While each solution has the potential to make some progress locally, integration at the enterprise level is often a challenge.
For example, while working with an organization primarily focused on retail, marketing optimization created promotional demands that exceeded inventory and staffing capacity. Each of these areas had its own intelligence, but there was no coordination of intelligence at the enterprise level.
The result was friction instead of acceleration during operation.
To future-proof enterprise applications, this piecemeal approach to AI won’t work. Enterprise apps need to become systems that unify signals, workflows, decisions, and execution.
It is essentially the difference between a company adopting AI or transforming.
AI Agent Enterprise Leadership Priorities
But as AI agents are integrated into enterprise systems, the focus of enterprise leaders must also change.
Leaders no longer need to think only about what kind of AI technology will be deployed.
Instead, you should ask yourself:
- What outcomes require better performance?
- What processes have too much friction?
- What decisions are best left to humans?
- Where does AI fit in for safe coordination?
- Who will govern and oversee how things work?
- How will success be tracked and measured?
And generally speaking, organizations that are making good progress tend to take an operational rather than a testing approach to AI.
They focus on operational efficiency, coordination, governance, and value rather than the use of cutting-edge AI.
These transformations are part of the bigger picture. Today’s companies realize that the way to gain a competitive edge is not simply by deploying AI systems, but by establishing an “AI operating model,” as proposed by IBM. In this model, AI agents work in conjunction with enterprise data, automation systems, governance, and human decision-making. As AI capabilities become more pervasive, the way companies design their operations around intelligent execution will become a competitive factor.
In fact, the best operating models I’ve observed combine human decision-making with AI coordination. Humans take the lead in some processes. AI makes suggestions for other processes, but managers make the final decisions. Finally, there may be certain repetitive operations that will eventually be executed independently using guardrails.
It’s all about intentionality.
Businesses of the future will operate differently than they have in the past.
Over time, all organizations will have access to AI models, cloud computing, and enterprise software systems on par with those used by other companies.
The difference lies in how well organizations embed intelligence within their workflows.
Successful organizations are those that can develop systems that do all of the following:
- Sensations change in the early stages of work
- Make decisions faster
- Reduce workflow friction
- Continuously learn based on results
- Embed AI investments directly within your business processes
AI agents can help make this happen.
But the bigger challenge goes beyond leveraging more AI.
This challenge involves changing the way businesses sense, decide, act, and learn operationally.
This is an ongoing evolution that will transform enterprise application software and enterprise operations in general.
This article is published as part of the Foundry Expert Contributor Network.
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